Supervised learning of lexical semantic verb classes using frequency distributions

Abstract

We report a number of computational experiments in supervised learning whose goal is to automatically classify a set of verbs into lexical semantic classes, based on frequency distribution approximations of grammatical features extracted from a very large annotated corpus. Distributions of five syntactic features that approximate transitivity alternations and thematic role assignments are sufficient to reduce error rate by 56% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpusdriven extraction of grammatical features is a promising methodology for automatic lexical acquisition